空间分布的仿生依从性使鲁棒拟人化机器人操作。

Kai Junge, Josie Hughes
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引用次数: 0

摘要

令人印象深刻的能力,人类稳健地执行操作源于兼容的交互,使之成为可能的结构和材料分布在手中。我们建议在拟人机械手中模仿这种空间分布的顺应性可以增强开环操作的鲁棒性,并导致类似人类的行为。这里我们介绍的ADAPT手,配备可配置的柔顺元件在皮肤,手指和手腕。在量化了针对刚性配置的单个组件的顺应性影响后,我们实验分析了全手的性能。通过自动拾取测试,我们表明抓取鲁棒性反映了估计的几何理论极限,而压力测试机器人执行800+抓取。最后,在受限的环境中,以93%的成功率抓住了24个不同几何形状的物品。我们证明了被动适应驱动的手-对象自组织行为支撑了这种鲁棒性。根据物体的几何形状,手表现出不同的抓取类型,与人类自然抓取的相似性为68%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatially distributed biomimetic compliance enables robust anthropomorphic robotic manipulation.

The impressive capabilities of humans to robustly perform manipulation stems from compliant interactions, enabled by the structure and materials distributed in the hands. We propose that mimicking this spatially distributed compliance in an anthropomorphic robotic hand enhances open-loop manipulation robustness and leads to human-like behaviors. Here we introduce the ADAPT Hand, equipped with configurable compliant elements on the skin, fingers, and wrist. After quantifying the effect of compliance on individual components against a rigid configuration, we experimentally analyze the performance of the full hand. Through automated pick-and-place tests, we show the grasping robustness mirrors the estimated geometric theoretical limit, while stress-testing the robot to perform 800+ grasps. Finally, 24 items with varying geometries are grasped in a constrained environment with a 93% success rate. We demonstrate that the hand-object self-organization behavior, driven by passive adaptation, underpins this robustness. The hand exhibits different grasp types based on object geometries, with a 68% similarity to natural human grasps.

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